Software Engineer I - AI and ML
9.0/10
Zinnia
$100,000 – $160,000 USD
Remote
mid
about 1 month ago
May be outdated
aitechPythonNumPyPandasFastAPIScikit-learnPyTorchTensorFlowXGBoostDBSCANLLMs
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Description
WHAT YOU'LL DO:
- •Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
- •Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
- •Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
- •Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
- •Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
- •Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.
Requirements
WHAT YOU'LL NEED
- •Python: Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
- •NumPy · Pandas · FastAPI · Scikit-learn
- •Machine Learning: Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
- •PyTorch · TensorFlow · XGBoost · DBSCAN
- •Generative AI (2+ yrs): Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
- •LLMs · RAG · Prompt Eng. · Fine-tuning
- •NLP & Search / Ranking: Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
- •spaCy · BERT · FAISS · Elasticsearch
- •API Development: Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
- •REST · FastAPI · OAuth2 · Swagger
- •Databases: Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
- •PostgreSQL · MongoDB · Vector DBs
- •GOOD TO HAVE: Cloud Platforms: Deploys and scales AI workloads on AWS, Azure, or GCP.
- •AWS · Azure
- •TypeScript / JavaScript: Frontend or full-stack exposure for building ML-powered product interfaces.
- •TypeScript · React · Node.js
- •MLOps: Manages the ML lifecycle — tracking, versioning, and pipeline automation.
- •MLflow · Kubeflow · CI/CD
- •Containerization & Orchestration: Packages and scales AI services using containers and cluster management.
- •Docker · Kubernetes
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